Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations9331
Missing cells1986
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 MiB
Average record size in memory1.2 KiB

Variable types

Text8
Categorical9

Variable descriptions

CODIGO{'description': 'Código único del establecimiento educativo.', 'unit': None}
DISTRITO{'description': 'Distrito en el que se encuentra el establecimiento educativo.', 'unit': None}
DEPARTAMENTO{'description': 'Departamento al que pertenece el establecimiento educativo.', 'unit': None}
MUNICIPIO{'description': 'Municipio en el que se encuentra el establecimiento educativo.', 'unit': None}
ESTABLECIMIENTO{'description': 'Nombre del establecimiento educativo.', 'unit': None}
DIRECCION{'description': 'Dirección física del establecimiento educativo.', 'unit': None}
TELEFONO{'description': 'Número de teléfono del establecimiento educativo.', 'unit': None}
SUPERVISOR{'description': 'Nombre del supervisor del establecimiento educativo.', 'unit': None}
DIRECTOR{'description': 'Nombre del director del establecimiento educativo.', 'unit': None}
NIVEL{'description': 'Nivel educativo ofrecido por el establecimiento (ej. Preprimaria, Primaria, Secundaria, Diversificado).', 'unit': None}
SECTOR{'description': 'Sector del establecimiento educativo (ej. Público, Privado).', 'unit': None}
AREA{'description': 'Área en la que se encuentra el establecimiento educativo (ej. Urbana, Rural).', 'unit': None}
STATUS{'description': 'Estado actual del establecimiento educativo (ej. Activo, Cerrado).', 'unit': None}
MODALIDAD{'description': 'Modalidad educativa ofrecida (ej. Regular, Nocturno).', 'unit': None}
JORNADA{'description': 'Jornada del establecimiento educativo (ej. Diurna, Vespertina).', 'unit': None}
PLAN{'description': 'Plan educativo del establecimiento (ej. Nacional, Internacional).', 'unit': None}
DEPARTAMENTAL{'description': 'Código o descripción del departamento al que se reporta el establecimiento.', 'unit': None}

Alerts

NIVEL has constant value "DIVERSIFICADO"Constant
DEPARTAMENTAL is highly overall correlated with DEPARTAMENTOHigh correlation
DEPARTAMENTO is highly overall correlated with DEPARTAMENTALHigh correlation
JORNADA is highly overall correlated with PLANHigh correlation
PLAN is highly overall correlated with JORNADAHigh correlation
SECTOR is highly imbalanced (61.6%)Imbalance
AREA is highly imbalanced (56.2%)Imbalance
STATUS is highly imbalanced (51.8%)Imbalance
MODALIDAD is highly imbalanced (79.9%)Imbalance
PLAN is highly imbalanced (55.4%)Imbalance
DISTRITO has 208 (2.2%) missing valuesMissing
TELEFONO has 611 (6.5%) missing valuesMissing
SUPERVISOR has 209 (2.2%) missing valuesMissing
DIRECTOR has 897 (9.6%) missing valuesMissing
CODIGO has unique valuesUnique

Reproduction

Analysis started2024-08-12 20:25:52.878300
Analysis finished2024-08-12 20:25:53.964311
Duration1.09 second
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

CODIGO
Text

UNIQUE 

{'description': 'Código único del establecimiento educativo.', 'unit': None}

Distinct9331
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size638.0 KiB
2024-08-12T14:25:54.071869image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters121303
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9331 ?
Unique (%)100.0%

Sample

1st row16-01-0138-46
2nd row16-01-0139-46
3rd row16-01-0140-46
4th row16-01-0141-46
5th row16-01-0142-46
ValueCountFrequency (%)
16-01-0138-46 1
 
< 0.1%
16-01-1176-46 1
 
< 0.1%
16-01-0559-46 1
 
< 0.1%
16-01-0150-46 1
 
< 0.1%
16-01-0140-46 1
 
< 0.1%
16-01-0141-46 1
 
< 0.1%
16-01-0142-46 1
 
< 0.1%
16-01-0143-46 1
 
< 0.1%
16-01-0145-46 1
 
< 0.1%
16-01-0147-46 1
 
< 0.1%
Other values (9321) 9321
99.9%
2024-08-12T14:25:54.254434image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 27993
23.1%
0 26140
21.5%
1 14943
12.3%
4 13300
11.0%
6 12742
10.5%
2 6579
 
5.4%
3 4685
 
3.9%
5 4067
 
3.4%
7 3722
 
3.1%
8 3698
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 93310
76.9%
Dash Punctuation 27993
 
23.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26140
28.0%
1 14943
16.0%
4 13300
14.3%
6 12742
13.7%
2 6579
 
7.1%
3 4685
 
5.0%
5 4067
 
4.4%
7 3722
 
4.0%
8 3698
 
4.0%
9 3434
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 27993
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121303
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 27993
23.1%
0 26140
21.5%
1 14943
12.3%
4 13300
11.0%
6 12742
10.5%
2 6579
 
5.4%
3 4685
 
3.9%
5 4067
 
3.4%
7 3722
 
3.1%
8 3698
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 27993
23.1%
0 26140
21.5%
1 14943
12.3%
4 13300
11.0%
6 12742
10.5%
2 6579
 
5.4%
3 4685
 
3.9%
5 4067
 
3.4%
7 3722
 
3.1%
8 3698
 
3.0%

DISTRITO
Text

MISSING 

{'description': 'Distrito en el que se encuentra el establecimiento educativo.', 'unit': None}

Distinct690
Distinct (%)7.6%
Missing208
Missing (%)2.2%
Memory size567.8 KiB
2024-08-12T14:25:54.430393image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9894771
Min length3

Characters and Unicode

Total characters54642
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique87 ?
Unique (%)1.0%

Sample

1st row16-031
2nd row16-031
3rd row16-031
4th row16-005
5th row16-005
ValueCountFrequency (%)
01-403 268
 
2.9%
11-017 176
 
1.9%
05-033 167
 
1.8%
01-411 167
 
1.8%
18-008 130
 
1.4%
05-007 104
 
1.1%
01-641 103
 
1.1%
18-039 102
 
1.1%
10-019 95
 
1.0%
03-002 95
 
1.0%
Other values (680) 7716
84.6%
2024-08-12T14:25:54.667034image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15825
29.0%
1 10914
20.0%
- 9123
16.7%
2 4175
 
7.6%
3 3570
 
6.5%
4 2597
 
4.8%
6 2144
 
3.9%
5 1772
 
3.2%
7 1671
 
3.1%
9 1571
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45519
83.3%
Dash Punctuation 9123
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15825
34.8%
1 10914
24.0%
2 4175
 
9.2%
3 3570
 
7.8%
4 2597
 
5.7%
6 2144
 
4.7%
5 1772
 
3.9%
7 1671
 
3.7%
9 1571
 
3.5%
8 1280
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 9123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 54642
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15825
29.0%
1 10914
20.0%
- 9123
16.7%
2 4175
 
7.6%
3 3570
 
6.5%
4 2597
 
4.8%
6 2144
 
3.9%
5 1772
 
3.2%
7 1671
 
3.1%
9 1571
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15825
29.0%
1 10914
20.0%
- 9123
16.7%
2 4175
 
7.6%
3 3570
 
6.5%
4 2597
 
4.8%
6 2144
 
3.9%
5 1772
 
3.2%
7 1671
 
3.1%
9 1571
 
2.9%

DEPARTAMENTO
Categorical

HIGH CORRELATION 

{'description': 'Departamento al que pertenece el establecimiento educativo.', 'unit': None}

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size615.5 KiB
CIUDAD CAPITAL
1564 
GUATEMALA
1479 
ESCUINTLA
628 
SAN MARCOS
574 
HUEHUETENANGO
516 
Other values (18)
4570 

Length

Max length14
Median length12
Mean length10.534348
Min length5

Characters and Unicode

Total characters98296
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTA VERAPAZ
2nd rowALTA VERAPAZ
3rd rowALTA VERAPAZ
4th rowALTA VERAPAZ
5th rowALTA VERAPAZ

Common Values

ValueCountFrequency (%)
CIUDAD CAPITAL 1564
16.8%
GUATEMALA 1479
15.9%
ESCUINTLA 628
 
6.7%
SAN MARCOS 574
 
6.2%
HUEHUETENANGO 516
 
5.5%
QUETZALTENANGO 491
 
5.3%
SUCHITEPEQUEZ 385
 
4.1%
ALTA VERAPAZ 374
 
4.0%
IZABAL 368
 
3.9%
PETEN 366
 
3.9%
Other values (13) 2586
27.7%

Length

2024-08-12T14:25:54.747288image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ciudad 1564
 
12.8%
capital 1564
 
12.8%
guatemala 1479
 
12.1%
escuintla 628
 
5.1%
san 574
 
4.7%
marcos 574
 
4.7%
huehuetenango 516
 
4.2%
quetzaltenango 491
 
4.0%
verapaz 488
 
4.0%
suchitepequez 385
 
3.1%
Other values (18) 3976
32.5%

Most occurring characters

ValueCountFrequency (%)
A 19074
19.4%
E 9454
 
9.6%
T 7935
 
8.1%
U 7809
 
7.9%
L 6617
 
6.7%
C 5990
 
6.1%
I 5852
 
6.0%
N 4637
 
4.7%
P 3891
 
4.0%
D 3128
 
3.2%
Other values (12) 23909
24.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 95388
97.0%
Space Separator 2908
 
3.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19074
20.0%
E 9454
9.9%
T 7935
 
8.3%
U 7809
 
8.2%
L 6617
 
6.9%
C 5990
 
6.3%
I 5852
 
6.1%
N 4637
 
4.9%
P 3891
 
4.1%
D 3128
 
3.3%
Other values (11) 21001
22.0%
Space Separator
ValueCountFrequency (%)
2908
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 95388
97.0%
Common 2908
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19074
20.0%
E 9454
9.9%
T 7935
 
8.3%
U 7809
 
8.2%
L 6617
 
6.9%
C 5990
 
6.3%
I 5852
 
6.1%
N 4637
 
4.9%
P 3891
 
4.1%
D 3128
 
3.3%
Other values (11) 21001
22.0%
Common
ValueCountFrequency (%)
2908
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98296
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19074
19.4%
E 9454
 
9.6%
T 7935
 
8.1%
U 7809
 
7.9%
L 6617
 
6.7%
C 5990
 
6.1%
I 5852
 
6.0%
N 4637
 
4.7%
P 3891
 
4.0%
D 3128
 
3.2%
Other values (12) 23909
24.3%

MUNICIPIO
Text

{'description': 'Municipio en el que se encuentra el establecimiento educativo.', 'unit': None}

Distinct328
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size620.6 KiB
2024-08-12T14:25:54.889507image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length27
Median length24
Mean length11.096881
Min length4

Characters and Unicode

Total characters103545
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.2%

Sample

1st rowCOBAN
2nd rowCOBAN
3rd rowCOBAN
4th rowCOBAN
5th rowCOBAN
ValueCountFrequency (%)
guatemala 1701
 
12.0%
san 1300
 
9.2%
villa 466
 
3.3%
nueva 436
 
3.1%
mixco 430
 
3.0%
santa 395
 
2.8%
la 317
 
2.2%
quetzaltenango 248
 
1.7%
sacatepequez 226
 
1.6%
retalhuleu 185
 
1.3%
Other values (329) 8502
59.8%
2024-08-12T14:25:55.128844image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 21019
20.3%
E 8428
 
8.1%
N 7461
 
7.2%
T 7397
 
7.1%
L 7277
 
7.0%
U 6695
 
6.5%
O 5146
 
5.0%
4875
 
4.7%
S 4809
 
4.6%
I 4806
 
4.6%
Other values (15) 25632
24.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 98670
95.3%
Space Separator 4875
 
4.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 21019
21.3%
E 8428
8.5%
N 7461
 
7.6%
T 7397
 
7.5%
L 7277
 
7.4%
U 6695
 
6.8%
O 5146
 
5.2%
S 4809
 
4.9%
I 4806
 
4.9%
C 4648
 
4.7%
Other values (14) 20984
21.3%
Space Separator
ValueCountFrequency (%)
4875
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 98670
95.3%
Common 4875
 
4.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 21019
21.3%
E 8428
8.5%
N 7461
 
7.6%
T 7397
 
7.5%
L 7277
 
7.4%
U 6695
 
6.8%
O 5146
 
5.2%
S 4809
 
4.9%
I 4806
 
4.9%
C 4648
 
4.7%
Other values (14) 20984
21.3%
Common
ValueCountFrequency (%)
4875
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103545
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 21019
20.3%
E 8428
 
8.1%
N 7461
 
7.2%
T 7397
 
7.1%
L 7277
 
7.0%
U 6695
 
6.5%
O 5146
 
5.0%
4875
 
4.7%
S 4809
 
4.6%
I 4806
 
4.6%
Other values (15) 25632
24.8%

ESTABLECIMIENTO
Text

{'description': 'Nombre del establecimiento educativo.', 'unit': None}

Distinct4473
Distinct (%)47.9%
Missing0
Missing (%)0.0%
Memory size881.7 KiB
2024-08-12T14:25:55.285701image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length125
Median length103
Mean length39.748687
Min length3

Characters and Unicode

Total characters370895
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2642 ?
Unique (%)28.3%

Sample

1st rowCOLEGIO COBAN
2nd rowCOLEGIO PARTICULAR MIXTO VERAPAZ
3rd rowCOLEGIO LA INMACULADA
4th rowESCUELA NACIONAL DE CIENCIAS COMERCIALES
5th rowINSTITUTO NORMAL MIXTO DEL NORTE EMILIO ROSALES PONCE
ValueCountFrequency (%)
de 3797
 
7.7%
colegio 3603
 
7.3%
mixto 2814
 
5.7%
instituto 2660
 
5.4%
liceo 1724
 
3.5%
educacion 1482
 
3.0%
privado 1430
 
2.9%
centro 1220
 
2.5%
diversificada 860
 
1.8%
educativo 803
 
1.6%
Other values (3110) 28631
58.4%
2024-08-12T14:25:55.536294image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39757
10.7%
I 39539
10.7%
O 37072
10.0%
E 33161
 
8.9%
A 31789
 
8.6%
C 26183
 
7.1%
T 23388
 
6.3%
N 21588
 
5.8%
L 17660
 
4.8%
R 17123
 
4.6%
Other values (37) 83635
22.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 328496
88.6%
Space Separator 39757
 
10.7%
Other Punctuation 972
 
0.3%
Dash Punctuation 877
 
0.2%
Decimal Number 379
 
0.1%
Open Punctuation 206
 
0.1%
Close Punctuation 205
 
0.1%
Modifier Symbol 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 39539
12.0%
O 37072
11.3%
E 33161
10.1%
A 31789
9.7%
C 26183
 
8.0%
T 23388
 
7.1%
N 21588
 
6.6%
L 17660
 
5.4%
R 17123
 
5.2%
D 14084
 
4.3%
Other values (16) 66909
20.4%
Decimal Number
ValueCountFrequency (%)
2 131
34.6%
0 72
19.0%
1 59
15.6%
3 37
 
9.8%
4 22
 
5.8%
7 20
 
5.3%
6 12
 
3.2%
8 10
 
2.6%
5 9
 
2.4%
9 7
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 836
86.0%
, 115
 
11.8%
& 9
 
0.9%
/ 9
 
0.9%
% 2
 
0.2%
# 1
 
0.1%
Space Separator
ValueCountFrequency (%)
39757
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 877
100.0%
Open Punctuation
ValueCountFrequency (%)
( 206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 205
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 328496
88.6%
Common 42399
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 39539
12.0%
O 37072
11.3%
E 33161
10.1%
A 31789
9.7%
C 26183
 
8.0%
T 23388
 
7.1%
N 21588
 
6.6%
L 17660
 
5.4%
R 17123
 
5.2%
D 14084
 
4.3%
Other values (16) 66909
20.4%
Common
ValueCountFrequency (%)
39757
93.8%
- 877
 
2.1%
. 836
 
2.0%
( 206
 
0.5%
) 205
 
0.5%
2 131
 
0.3%
, 115
 
0.3%
0 72
 
0.2%
1 59
 
0.1%
3 37
 
0.1%
Other values (11) 104
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 370895
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39757
10.7%
I 39539
10.7%
O 37072
10.0%
E 33161
 
8.9%
A 31789
 
8.6%
C 26183
 
7.1%
T 23388
 
6.3%
N 21588
 
5.8%
L 17660
 
4.8%
R 17123
 
4.6%
Other values (37) 83635
22.5%

DIRECCION
Text

{'description': 'Dirección física del establecimiento educativo.', 'unit': None}

Distinct5517
Distinct (%)59.5%
Missing61
Missing (%)0.7%
Memory size764.3 KiB
2024-08-12T14:25:55.747892image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length121
Median length91
Mean length27.202913
Min length3

Characters and Unicode

Total characters252171
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3816 ?
Unique (%)41.2%

Sample

1st rowKM2 SALIDA A SAN JUAN CHAMELCO ZONA 8
2nd rowKM 2095 ENTRADA A LA CIUDAD
3rd row7A AVENIDA 11109 ZONA 6
4th row2A CALLE 1110 ZONA 2
5th row3A AVE 623 ZONA 11
ValueCountFrequency (%)
zona 4153
 
8.4%
calle 2926
 
5.9%
avenida 2280
 
4.6%
1 1812
 
3.6%
barrio 1128
 
2.3%
aldea 1113
 
2.2%
colonia 1110
 
2.2%
el 958
 
1.9%
san 918
 
1.8%
2 702
 
1.4%
Other values (3505) 32618
65.6%
2024-08-12T14:25:56.071798image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
40481
16.1%
A 38758
15.4%
E 17117
 
6.8%
L 16564
 
6.6%
N 16007
 
6.3%
O 15866
 
6.3%
I 12172
 
4.8%
C 10695
 
4.2%
R 9497
 
3.8%
1 7036
 
2.8%
Other values (29) 67978
27.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 180801
71.7%
Space Separator 40481
 
16.1%
Decimal Number 30865
 
12.2%
Lowercase Letter 24
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 38758
21.4%
E 17117
9.5%
L 16564
9.2%
N 16007
8.9%
O 15866
8.8%
I 12172
 
6.7%
C 10695
 
5.9%
R 9497
 
5.3%
D 6502
 
3.6%
T 5909
 
3.3%
Other values (16) 31714
17.5%
Decimal Number
ValueCountFrequency (%)
1 7036
22.8%
2 4337
14.1%
3 3696
12.0%
4 3144
10.2%
5 2917
9.5%
0 2514
 
8.1%
6 2195
 
7.1%
7 1897
 
6.1%
8 1581
 
5.1%
9 1548
 
5.0%
Lowercase Letter
ValueCountFrequency (%)
a 21
87.5%
o 3
 
12.5%
Space Separator
ValueCountFrequency (%)
40481
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 180825
71.7%
Common 71346
 
28.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 38758
21.4%
E 17117
9.5%
L 16564
9.2%
N 16007
8.9%
O 15866
8.8%
I 12172
 
6.7%
C 10695
 
5.9%
R 9497
 
5.3%
D 6502
 
3.6%
T 5909
 
3.3%
Other values (18) 31738
17.6%
Common
ValueCountFrequency (%)
40481
56.7%
1 7036
 
9.9%
2 4337
 
6.1%
3 3696
 
5.2%
4 3144
 
4.4%
5 2917
 
4.1%
0 2514
 
3.5%
6 2195
 
3.1%
7 1897
 
2.7%
8 1581
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252171
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
40481
16.1%
A 38758
15.4%
E 17117
 
6.8%
L 16564
 
6.6%
N 16007
 
6.3%
O 15866
 
6.3%
I 12172
 
4.8%
C 10695
 
4.2%
R 9497
 
3.8%
1 7036
 
2.8%
Other values (29) 67978
27.0%

TELEFONO
Text

MISSING 

{'description': 'Número de teléfono del establecimiento educativo.', 'unit': None}

Distinct5369
Distinct (%)61.6%
Missing611
Missing (%)6.5%
Memory size575.7 KiB
2024-08-12T14:25:56.240951image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length26
Median length8
Mean length8.3480505
Min length3

Characters and Unicode

Total characters72795
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3643 ?
Unique (%)41.8%

Sample

1st row77945104
2nd row77367402
3rd row78232301
4th row79514215
5th row79521468
ValueCountFrequency (%)
22067425 21
 
0.2%
79480009 14
 
0.2%
77602663 13
 
0.1%
22093200 12
 
0.1%
77746400 11
 
0.1%
45353648 11
 
0.1%
59304894 11
 
0.1%
22322912 10
 
0.1%
78899679 10
 
0.1%
24637777 10
 
0.1%
Other values (5405) 8705
98.6%
2024-08-12T14:25:56.479531image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7 8999
12.4%
2 8505
11.7%
4 7891
10.8%
5 7660
10.5%
0 7479
10.3%
3 7101
9.8%
8 6286
8.6%
6 6166
8.5%
1 5924
8.1%
9 5632
7.7%
Other values (2) 1152
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 71643
98.4%
Other Punctuation 1044
 
1.4%
Space Separator 108
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 8999
12.6%
2 8505
11.9%
4 7891
11.0%
5 7660
10.7%
0 7479
10.4%
3 7101
9.9%
8 6286
8.8%
6 6166
8.6%
1 5924
8.3%
9 5632
7.9%
Other Punctuation
ValueCountFrequency (%)
. 1044
100.0%
Space Separator
ValueCountFrequency (%)
108
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72795
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 8999
12.4%
2 8505
11.7%
4 7891
10.8%
5 7660
10.5%
0 7479
10.3%
3 7101
9.8%
8 6286
8.6%
6 6166
8.5%
1 5924
8.1%
9 5632
7.7%
Other values (2) 1152
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 8999
12.4%
2 8505
11.7%
4 7891
10.8%
5 7660
10.5%
0 7479
10.3%
3 7101
9.8%
8 6286
8.6%
6 6166
8.5%
1 5924
8.1%
9 5632
7.7%
Other values (2) 1152
 
1.6%

SUPERVISOR
Text

MISSING 

{'description': 'Nombre del supervisor del establecimiento educativo.', 'unit': None}

Distinct658
Distinct (%)7.2%
Missing209
Missing (%)2.2%
Memory size813.9 KiB
2024-08-12T14:25:56.604482image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length63
Median length44
Mean length28.894541
Min length14

Characters and Unicode

Total characters263576
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique75 ?
Unique (%)0.8%

Sample

1st rowMERCEDES JOSEFINA TORRES GALVEZ
2nd rowMERCEDES JOSEFINA TORRES GALVEZ
3rd rowMERCEDES JOSEFINA TORRES GALVEZ
4th rowRUDY ADOLFO TOT OCH
5th rowRUDY ADOLFO TOT OCH
ValueCountFrequency (%)
de 2167
 
5.5%
martinez 604
 
1.5%
lopez 567
 
1.4%
leon 549
 
1.4%
juan 544
 
1.4%
gonzalez 511
 
1.3%
carlos 443
 
1.1%
morales 408
 
1.0%
perez 398
 
1.0%
humberto 388
 
1.0%
Other values (1213) 32556
83.2%
2024-08-12T14:25:56.810302image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 32945
12.5%
30013
11.4%
E 25176
 
9.6%
R 20656
 
7.8%
O 20352
 
7.7%
I 16909
 
6.4%
L 16653
 
6.3%
N 15039
 
5.7%
S 10993
 
4.2%
D 8966
 
3.4%
Other values (25) 65874
25.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 233433
88.6%
Space Separator 30013
 
11.4%
Dash Punctuation 124
 
< 0.1%
Other Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 32945
14.1%
E 25176
10.8%
R 20656
 
8.8%
O 20352
 
8.7%
I 16909
 
7.2%
L 16653
 
7.1%
N 15039
 
6.4%
S 10993
 
4.7%
D 8966
 
3.8%
C 8652
 
3.7%
Other values (22) 57092
24.5%
Space Separator
ValueCountFrequency (%)
30013
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Other Punctuation
ValueCountFrequency (%)
. 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 233433
88.6%
Common 30143
 
11.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 32945
14.1%
E 25176
10.8%
R 20656
 
8.8%
O 20352
 
8.7%
I 16909
 
7.2%
L 16653
 
7.1%
N 15039
 
6.4%
S 10993
 
4.7%
D 8966
 
3.8%
C 8652
 
3.7%
Other values (22) 57092
24.5%
Common
ValueCountFrequency (%)
30013
99.6%
- 124
 
0.4%
. 6
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262583
99.6%
None 993
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 32945
12.5%
30013
11.4%
E 25176
 
9.6%
R 20656
 
7.9%
O 20352
 
7.8%
I 16909
 
6.4%
L 16653
 
6.3%
N 15039
 
5.7%
S 10993
 
4.2%
D 8966
 
3.4%
Other values (19) 64881
24.7%
None
ValueCountFrequency (%)
Ñ 354
35.6%
Ó 229
23.1%
É 156
15.7%
Á 122
 
12.3%
Í 104
 
10.5%
Ú 28
 
2.8%

DIRECTOR
Text

MISSING 

{'description': 'Nombre del director del establecimiento educativo.', 'unit': None}

Distinct4677
Distinct (%)55.5%
Missing897
Missing (%)9.6%
Memory size945.4 KiB
2024-08-12T14:25:56.991096image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length51
Median length43
Mean length26.814679
Min length1

Characters and Unicode

Total characters226155
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2948 ?
Unique (%)35.0%

Sample

1st rowGUSTAVO ADOLFO SIERRA POP
2nd rowGILMA DOLORES GUAY PAZ DE LEAL
3rd rowVIRGINIA SOLANO SERRANO
4th rowHÉCTOR ROLANDO CHUN POOU
5th rowVICTOR HUGO DOMÍNGUEZ REYES
ValueCountFrequency (%)
de 544
 
1.7%
lópez 521
 
1.6%
morales 320
 
1.0%
hernández 265
 
0.8%
garcía 249
 
0.8%
maría 242
 
0.7%
pérez 234
 
0.7%
215
 
0.7%
gonzález 215
 
0.7%
josé 184
 
0.6%
Other values (4021) 29924
90.9%
2024-08-12T14:25:57.255352image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 28642
12.7%
24479
 
10.8%
E 19732
 
8.7%
R 18102
 
8.0%
O 15080
 
6.7%
L 14006
 
6.2%
I 13884
 
6.1%
N 12936
 
5.7%
S 9378
 
4.1%
D 7365
 
3.3%
Other values (36) 62551
27.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 200597
88.7%
Space Separator 24479
 
10.8%
Dash Punctuation 1026
 
0.5%
Other Punctuation 49
 
< 0.1%
Modifier Symbol 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 28642
14.3%
E 19732
 
9.8%
R 18102
 
9.0%
O 15080
 
7.5%
L 14006
 
7.0%
I 13884
 
6.9%
N 12936
 
6.4%
S 9378
 
4.7%
D 7365
 
3.7%
C 7172
 
3.6%
Other values (26) 54300
27.1%
Other Punctuation
ValueCountFrequency (%)
. 45
91.8%
, 2
 
4.1%
/ 1
 
2.0%
" 1
 
2.0%
Modifier Symbol
ValueCountFrequency (%)
´ 1
50.0%
` 1
50.0%
Space Separator
ValueCountFrequency (%)
24479
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1026
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 200597
88.7%
Common 25558
 
11.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 28642
14.3%
E 19732
 
9.8%
R 18102
 
9.0%
O 15080
 
7.5%
L 14006
 
7.0%
I 13884
 
6.9%
N 12936
 
6.4%
S 9378
 
4.7%
D 7365
 
3.7%
C 7172
 
3.6%
Other values (26) 54300
27.1%
Common
ValueCountFrequency (%)
24479
95.8%
- 1026
 
4.0%
. 45
 
0.2%
, 2
 
< 0.1%
´ 1
 
< 0.1%
/ 1
 
< 0.1%
( 1
 
< 0.1%
) 1
 
< 0.1%
` 1
 
< 0.1%
" 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 219906
97.2%
None 6249
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 28642
13.0%
24479
11.1%
E 19732
 
9.0%
R 18102
 
8.2%
O 15080
 
6.9%
L 14006
 
6.4%
I 13884
 
6.3%
N 12936
 
5.9%
S 9378
 
4.3%
D 7365
 
3.3%
Other values (25) 56302
25.6%
None
ValueCountFrequency (%)
Í 1631
26.1%
Á 1488
23.8%
Ó 1379
22.1%
É 1175
18.8%
Ñ 278
 
4.4%
Ú 259
 
4.1%
Ü 28
 
0.4%
È 5
 
0.1%
Ò 4
 
0.1%
Û 1
 
< 0.1%

NIVEL
Categorical

CONSTANT 

{'description': 'Nivel educativo ofrecido por el establecimiento (ej. Preprimaria, Primaria, Secundaria, Diversificado).', 'unit': None}

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size638.0 KiB
DIVERSIFICADO
9331 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters121303
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIVERSIFICADO
2nd rowDIVERSIFICADO
3rd rowDIVERSIFICADO
4th rowDIVERSIFICADO
5th rowDIVERSIFICADO

Common Values

ValueCountFrequency (%)
DIVERSIFICADO 9331
100.0%

Length

2024-08-12T14:25:57.331230image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T14:25:57.378723image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
diversificado 9331
100.0%

Most occurring characters

ValueCountFrequency (%)
I 27993
23.1%
D 18662
15.4%
V 9331
 
7.7%
E 9331
 
7.7%
R 9331
 
7.7%
S 9331
 
7.7%
F 9331
 
7.7%
C 9331
 
7.7%
A 9331
 
7.7%
O 9331
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 121303
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 27993
23.1%
D 18662
15.4%
V 9331
 
7.7%
E 9331
 
7.7%
R 9331
 
7.7%
S 9331
 
7.7%
F 9331
 
7.7%
C 9331
 
7.7%
A 9331
 
7.7%
O 9331
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 121303
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 27993
23.1%
D 18662
15.4%
V 9331
 
7.7%
E 9331
 
7.7%
R 9331
 
7.7%
S 9331
 
7.7%
F 9331
 
7.7%
C 9331
 
7.7%
A 9331
 
7.7%
O 9331
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121303
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 27993
23.1%
D 18662
15.4%
V 9331
 
7.7%
E 9331
 
7.7%
R 9331
 
7.7%
S 9331
 
7.7%
F 9331
 
7.7%
C 9331
 
7.7%
A 9331
 
7.7%
O 9331
 
7.7%

SECTOR
Categorical

IMBALANCE 

{'description': 'Sector del establecimiento educativo (ej. Público, Privado).', 'unit': None}

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size584.5 KiB
PRIVADO
7955 
OFICIAL
990 
COOPERATIVA
 
245
MUNICIPAL
 
141

Length

Max length11
Median length7
Mean length7.1352481
Min length7

Characters and Unicode

Total characters66579
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPRIVADO
2nd rowPRIVADO
3rd rowPRIVADO
4th rowOFICIAL
5th rowOFICIAL

Common Values

ValueCountFrequency (%)
PRIVADO 7955
85.3%
OFICIAL 990
 
10.6%
COOPERATIVA 245
 
2.6%
MUNICIPAL 141
 
1.5%

Length

2024-08-12T14:25:57.436246image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T14:25:57.498905image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
privado 7955
85.3%
oficial 990
 
10.6%
cooperativa 245
 
2.6%
municipal 141
 
1.5%

Most occurring characters

ValueCountFrequency (%)
I 10462
15.7%
A 9576
14.4%
O 9435
14.2%
P 8341
12.5%
R 8200
12.3%
V 8200
12.3%
D 7955
11.9%
C 1376
 
2.1%
L 1131
 
1.7%
F 990
 
1.5%
Other values (5) 913
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66579
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 10462
15.7%
A 9576
14.4%
O 9435
14.2%
P 8341
12.5%
R 8200
12.3%
V 8200
12.3%
D 7955
11.9%
C 1376
 
2.1%
L 1131
 
1.7%
F 990
 
1.5%
Other values (5) 913
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 66579
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 10462
15.7%
A 9576
14.4%
O 9435
14.2%
P 8341
12.5%
R 8200
12.3%
V 8200
12.3%
D 7955
11.9%
C 1376
 
2.1%
L 1131
 
1.7%
F 990
 
1.5%
Other values (5) 913
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 10462
15.7%
A 9576
14.4%
O 9435
14.2%
P 8341
12.5%
R 8200
12.3%
V 8200
12.3%
D 7955
11.9%
C 1376
 
2.1%
L 1131
 
1.7%
F 990
 
1.5%
Other values (5) 913
 
1.4%

AREA
Categorical

IMBALANCE 

{'description': 'Área en la que se encuentra el establecimiento educativo (ej. Urbana, Rural).', 'unit': None}

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size572.5 KiB
URBANA
7603 
RURAL
1726 
SIN ESPECIFICAR
 
2

Length

Max length15
Median length6
Mean length5.8169542
Min length5

Characters and Unicode

Total characters54278
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowURBANA
2nd rowURBANA
3rd rowURBANA
4th rowURBANA
5th rowURBANA

Common Values

ValueCountFrequency (%)
URBANA 7603
81.5%
RURAL 1726
 
18.5%
SIN ESPECIFICAR 2
 
< 0.1%

Length

2024-08-12T14:25:57.559274image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T14:25:57.608103image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
urbana 7603
81.5%
rural 1726
 
18.5%
sin 2
 
< 0.1%
especificar 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 16934
31.2%
R 11057
20.4%
U 9329
17.2%
N 7605
14.0%
B 7603
14.0%
L 1726
 
3.2%
I 6
 
< 0.1%
S 4
 
< 0.1%
E 4
 
< 0.1%
C 4
 
< 0.1%
Other values (3) 6
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 54276
> 99.9%
Space Separator 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 16934
31.2%
R 11057
20.4%
U 9329
17.2%
N 7605
14.0%
B 7603
14.0%
L 1726
 
3.2%
I 6
 
< 0.1%
S 4
 
< 0.1%
E 4
 
< 0.1%
C 4
 
< 0.1%
Other values (2) 4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54276
> 99.9%
Common 2
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 16934
31.2%
R 11057
20.4%
U 9329
17.2%
N 7605
14.0%
B 7603
14.0%
L 1726
 
3.2%
I 6
 
< 0.1%
S 4
 
< 0.1%
E 4
 
< 0.1%
C 4
 
< 0.1%
Other values (2) 4
 
< 0.1%
Common
ValueCountFrequency (%)
2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 16934
31.2%
R 11057
20.4%
U 9329
17.2%
N 7605
14.0%
B 7603
14.0%
L 1726
 
3.2%
I 6
 
< 0.1%
S 4
 
< 0.1%
E 4
 
< 0.1%
C 4
 
< 0.1%
Other values (3) 6
 
< 0.1%

STATUS
Categorical

IMBALANCE 

{'description': 'Estado actual del establecimiento educativo (ej. Activo, Cerrado).', 'unit': None}

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size620.8 KiB
ABIERTA
6544 
CERRADA TEMPORALMENTE
2657 
TEMPORAL TITULOS
 
127
TEMPORAL NOMBRAMIENTO
 
3

Length

Max length21
Median length7
Mean length11.113493
Min length7

Characters and Unicode

Total characters103700
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowABIERTA
2nd rowABIERTA
3rd rowABIERTA
4th rowABIERTA
5th rowABIERTA

Common Values

ValueCountFrequency (%)
ABIERTA 6544
70.1%
CERRADA TEMPORALMENTE 2657
28.5%
TEMPORAL TITULOS 127
 
1.4%
TEMPORAL NOMBRAMIENTO 3
 
< 0.1%

Length

2024-08-12T14:25:57.669045image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T14:25:57.728221image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
abierta 6544
54.0%
cerrada 2657
21.9%
temporalmente 2657
21.9%
temporal 130
 
1.1%
titulos 127
 
1.0%
nombramiento 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
A 21192
20.4%
E 17305
16.7%
R 14648
14.1%
T 12245
11.8%
I 6674
 
6.4%
B 6547
 
6.3%
M 5450
 
5.3%
O 2920
 
2.8%
L 2914
 
2.8%
2787
 
2.7%
Other values (6) 11018
10.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 100913
97.3%
Space Separator 2787
 
2.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 21192
21.0%
E 17305
17.1%
R 14648
14.5%
T 12245
12.1%
I 6674
 
6.6%
B 6547
 
6.5%
M 5450
 
5.4%
O 2920
 
2.9%
L 2914
 
2.9%
P 2787
 
2.8%
Other values (5) 8231
 
8.2%
Space Separator
ValueCountFrequency (%)
2787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 100913
97.3%
Common 2787
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 21192
21.0%
E 17305
17.1%
R 14648
14.5%
T 12245
12.1%
I 6674
 
6.6%
B 6547
 
6.5%
M 5450
 
5.4%
O 2920
 
2.9%
L 2914
 
2.9%
P 2787
 
2.8%
Other values (5) 8231
 
8.2%
Common
ValueCountFrequency (%)
2787
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 21192
20.4%
E 17305
16.7%
R 14648
14.1%
T 12245
11.8%
I 6674
 
6.4%
B 6547
 
6.3%
M 5450
 
5.3%
O 2920
 
2.8%
L 2914
 
2.8%
2787
 
2.7%
Other values (6) 11018
10.6%

MODALIDAD
Categorical

IMBALANCE 

{'description': 'Modalidad educativa ofrecida (ej. Regular, Nocturno).', 'unit': None}

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size610.1 KiB
MONOLINGUE
9039 
BILINGUE
 
292

Length

Max length10
Median length10
Mean length9.9374129
Min length8

Characters and Unicode

Total characters92726
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMONOLINGUE
2nd rowMONOLINGUE
3rd rowMONOLINGUE
4th rowMONOLINGUE
5th rowBILINGUE

Common Values

ValueCountFrequency (%)
MONOLINGUE 9039
96.9%
BILINGUE 292
 
3.1%

Length

2024-08-12T14:25:57.793816image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T14:25:57.854022image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
monolingue 9039
96.9%
bilingue 292
 
3.1%

Most occurring characters

ValueCountFrequency (%)
N 18370
19.8%
O 18078
19.5%
I 9623
10.4%
L 9331
10.1%
G 9331
10.1%
U 9331
10.1%
E 9331
10.1%
M 9039
9.7%
B 292
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 92726
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 18370
19.8%
O 18078
19.5%
I 9623
10.4%
L 9331
10.1%
G 9331
10.1%
U 9331
10.1%
E 9331
10.1%
M 9039
9.7%
B 292
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 92726
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 18370
19.8%
O 18078
19.5%
I 9623
10.4%
L 9331
10.1%
G 9331
10.1%
U 9331
10.1%
E 9331
10.1%
M 9039
9.7%
B 292
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92726
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 18370
19.8%
O 18078
19.5%
I 9623
10.4%
L 9331
10.1%
G 9331
10.1%
U 9331
10.1%
E 9331
10.1%
M 9039
9.7%
B 292
 
0.3%

JORNADA
Categorical

HIGH CORRELATION 

{'description': 'Jornada del establecimiento educativo (ej. Diurna, Vespertina).', 'unit': None}

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size591.5 KiB
DOBLE
3039 
VESPERTINA
2567 
MATUTINA
2381 
SIN JORNADA
964 
NOCTURNA
 
289

Length

Max length11
Median length10
Mean length7.9025828
Min length5

Characters and Unicode

Total characters73739
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMATUTINA
2nd rowMATUTINA
3rd rowMATUTINA
4th rowMATUTINA
5th rowVESPERTINA

Common Values

ValueCountFrequency (%)
DOBLE 3039
32.6%
VESPERTINA 2567
27.5%
MATUTINA 2381
25.5%
SIN JORNADA 964
 
10.3%
NOCTURNA 289
 
3.1%
INTERMEDIA 91
 
1.0%

Length

2024-08-12T14:25:57.915496image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-08-12T14:25:57.977538image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
ValueCountFrequency (%)
doble 3039
29.5%
vespertina 2567
24.9%
matutina 2381
23.1%
sin 964
 
9.4%
jornada 964
 
9.4%
nocturna 289
 
2.8%
intermedia 91
 
0.9%

Most occurring characters

ValueCountFrequency (%)
A 9637
13.1%
E 8355
11.3%
T 7709
10.5%
N 7545
10.2%
I 6094
 
8.3%
O 4292
 
5.8%
D 4094
 
5.6%
R 3911
 
5.3%
S 3531
 
4.8%
L 3039
 
4.1%
Other values (8) 15532
21.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 72775
98.7%
Space Separator 964
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 9637
13.2%
E 8355
11.5%
T 7709
10.6%
N 7545
10.4%
I 6094
8.4%
O 4292
 
5.9%
D 4094
 
5.6%
R 3911
 
5.4%
S 3531
 
4.9%
L 3039
 
4.2%
Other values (7) 14568
20.0%
Space Separator
ValueCountFrequency (%)
964
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72775
98.7%
Common 964
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 9637
13.2%
E 8355
11.5%
T 7709
10.6%
N 7545
10.4%
I 6094
8.4%
O 4292
 
5.9%
D 4094
 
5.6%
R 3911
 
5.4%
S 3531
 
4.9%
L 3039
 
4.2%
Other values (7) 14568
20.0%
Common
ValueCountFrequency (%)
964
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 9637
13.1%
E 8355
11.3%
T 7709
10.5%
N 7545
10.2%
I 6094
 
8.3%
O 4292
 
5.8%
D 4094
 
5.6%
R 3911
 
5.3%
S 3531
 
4.8%
L 3039
 
4.1%
Other values (8) 15532
21.1%

PLAN
Categorical

HIGH CORRELATION  IMBALANCE 

{'description': 'Plan educativo del establecimiento (ej. Nacional, Internacional).', 'unit': None}

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size694.0 KiB
DIARIO(REGULAR)
5719 
FIN DE SEMANA
2330 
SEMIPRESENCIAL (FIN DE SEMANA)
 
481
SEMIPRESENCIAL (UN DÍA A LA SEMANA)
 
398
A DISTANCIA
 
143
Other values (8)
 
260

Length

Max length37
Median length15
Mean length16.163862
Min length5

Characters and Unicode

Total characters150825
Distinct characters23
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDIARIO(REGULAR)
2nd rowDIARIO(REGULAR)
3rd rowDIARIO(REGULAR)
4th rowDIARIO(REGULAR)
5th rowDIARIO(REGULAR)

Common Values

ValueCountFrequency (%)
DIARIO(REGULAR) 5719
61.3%
FIN DE SEMANA 2330
25.0%
SEMIPRESENCIAL (FIN DE SEMANA) 481
 
5.2%
SEMIPRESENCIAL (UN DÍA A LA SEMANA) 398
 
4.3%
A DISTANCIA 143
 
1.5%
SEMIPRESENCIAL 86
 
0.9%
SEMIPRESENCIAL (DOS DÍAS A LA SEMANA) 57
 
0.6%
VIRTUAL A DISTANCIA 50
 
0.5%
SABATINO 41
 
0.4%
DOMINICAL 19
 
0.2%
Other values (3) 7
 
0.1%

Length

2024-08-12T14:25:58.054181image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
diario(regular 5719
31.9%
semana 3266
18.2%
fin 2811
15.7%
de 2811
15.7%
semipresencial 1022
 
5.7%
a 648
 
3.6%
la 455
 
2.5%
un 398
 
2.2%
día 398
 
2.2%
distancia 193
 
1.1%
Other values (8) 231
 
1.3%

Most occurring characters

ValueCountFrequency (%)
A 21093
14.0%
R 18237
12.1%
I 16815
11.1%
E 14866
9.9%
D 9256
 
6.1%
8621
 
5.7%
N 7752
 
5.1%
L 7269
 
4.8%
( 6655
 
4.4%
) 6655
 
4.4%
Other values (13) 33606
22.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 128894
85.5%
Space Separator 8621
 
5.7%
Open Punctuation 6655
 
4.4%
Close Punctuation 6655
 
4.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 21093
16.4%
R 18237
14.1%
I 16815
13.0%
E 14866
11.5%
D 9256
7.2%
N 7752
 
6.0%
L 7269
 
5.6%
U 6169
 
4.8%
O 5841
 
4.5%
G 5721
 
4.4%
Other values (10) 15875
12.3%
Space Separator
ValueCountFrequency (%)
8621
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6655
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6655
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128894
85.5%
Common 21931
 
14.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 21093
16.4%
R 18237
14.1%
I 16815
13.0%
E 14866
11.5%
D 9256
7.2%
N 7752
 
6.0%
L 7269
 
5.6%
U 6169
 
4.8%
O 5841
 
4.5%
G 5721
 
4.4%
Other values (10) 15875
12.3%
Common
ValueCountFrequency (%)
8621
39.3%
( 6655
30.3%
) 6655
30.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 150370
99.7%
None 455
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 21093
14.0%
R 18237
12.1%
I 16815
11.2%
E 14866
9.9%
D 9256
 
6.2%
8621
 
5.7%
N 7752
 
5.2%
L 7269
 
4.8%
( 6655
 
4.4%
) 6655
 
4.4%
Other values (12) 33151
22.0%
None
ValueCountFrequency (%)
Í 455
100.0%

DEPARTAMENTAL
Categorical

HIGH CORRELATION 

{'description': 'Código o descripción del departamento al que se reporta el establecimiento.', 'unit': None}

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size628.1 KiB
GUATEMALA NORTE
1050 
GUATEMALA SUR
833 
GUATEMALA OCCIDENTE
787 
ESCUINTLA
628 
SAN MARCOS
574 
Other values (21)
5459 

Length

Max length19
Median length14
Mean length11.918122
Min length5

Characters and Unicode

Total characters111208
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowALTA VERAPAZ
2nd rowALTA VERAPAZ
3rd rowALTA VERAPAZ
4th rowALTA VERAPAZ
5th rowALTA VERAPAZ

Common Values

ValueCountFrequency (%)
GUATEMALA NORTE 1050
 
11.3%
GUATEMALA SUR 833
 
8.9%
GUATEMALA OCCIDENTE 787
 
8.4%
ESCUINTLA 628
 
6.7%
SAN MARCOS 574
 
6.2%
HUEHUETENANGO 516
 
5.5%
QUETZALTENANGO 491
 
5.3%
SUCHITEPEQUEZ 385
 
4.1%
ALTA VERAPAZ 374
 
4.0%
GUATEMALA ORIENTE 373
 
4.0%
Other values (16) 3320
35.6%

Length

2024-08-12T14:25:58.129753image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guatemala 3043
22.1%
norte 1091
 
7.9%
sur 833
 
6.1%
occidente 787
 
5.7%
escuintla 628
 
4.6%
san 574
 
4.2%
marcos 574
 
4.2%
huehuetenango 516
 
3.8%
quetzaltenango 491
 
3.6%
verapaz 488
 
3.5%
Other values (20) 4734
34.4%

Most occurring characters

ValueCountFrequency (%)
A 19074
17.2%
E 14429
13.0%
T 10186
 
9.2%
U 8642
 
7.8%
N 6888
 
6.2%
L 6617
 
6.0%
O 5054
 
4.5%
G 4534
 
4.1%
C 4436
 
4.0%
4428
 
4.0%
Other values (12) 26920
24.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 106780
96.0%
Space Separator 4428
 
4.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 19074
17.9%
E 14429
13.5%
T 10186
9.5%
U 8642
 
8.1%
N 6888
 
6.5%
L 6617
 
6.2%
O 5054
 
4.7%
G 4534
 
4.2%
C 4436
 
4.2%
M 4146
 
3.9%
Other values (11) 22774
21.3%
Space Separator
ValueCountFrequency (%)
4428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 106780
96.0%
Common 4428
 
4.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 19074
17.9%
E 14429
13.5%
T 10186
9.5%
U 8642
 
8.1%
N 6888
 
6.5%
L 6617
 
6.2%
O 5054
 
4.7%
G 4534
 
4.2%
C 4436
 
4.2%
M 4146
 
3.9%
Other values (11) 22774
21.3%
Common
ValueCountFrequency (%)
4428
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 111208
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 19074
17.2%
E 14429
13.0%
T 10186
 
9.2%
U 8642
 
7.8%
N 6888
 
6.2%
L 6617
 
6.0%
O 5054
 
4.5%
G 4534
 
4.1%
C 4436
 
4.0%
4428
 
4.0%
Other values (12) 26920
24.2%

Correlations

2024-08-12T14:25:58.177012image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
AREADEPARTAMENTALDEPARTAMENTOJORNADAMODALIDADPLANSECTORSTATUS
AREA1.0000.2130.2220.0880.1280.0730.1490.038
DEPARTAMENTAL0.2131.0000.9840.1350.2990.1230.1660.136
DEPARTAMENTO0.2220.9841.0000.1350.2930.1170.1640.140
JORNADA0.0880.1350.1351.0000.0970.5580.1480.166
MODALIDAD0.1280.2990.2930.0971.0000.0820.1530.003
PLAN0.0730.1230.1170.5580.0821.0000.1430.153
SECTOR0.1490.1660.1640.1480.1530.1431.0000.084
STATUS0.0380.1360.1400.1660.0030.1530.0841.000

Missing values

2024-08-12T14:25:53.679632image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T14:25:53.811392image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-08-12T14:25:53.905705image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CODIGODISTRITODEPARTAMENTOMUNICIPIOESTABLECIMIENTODIRECCIONTELEFONOSUPERVISORDIRECTORNIVELSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTAL
016-01-0138-4616-031ALTA VERAPAZCOBANCOLEGIO COBANKM2 SALIDA A SAN JUAN CHAMELCO ZONA 877945104MERCEDES JOSEFINA TORRES GALVEZGUSTAVO ADOLFO SIERRA POPDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZ
116-01-0139-4616-031ALTA VERAPAZCOBANCOLEGIO PARTICULAR MIXTO VERAPAZKM 2095 ENTRADA A LA CIUDAD77367402MERCEDES JOSEFINA TORRES GALVEZGILMA DOLORES GUAY PAZ DE LEALDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZ
216-01-0140-4616-031ALTA VERAPAZCOBANCOLEGIO LA INMACULADA7A AVENIDA 11109 ZONA 678232301MERCEDES JOSEFINA TORRES GALVEZVIRGINIA SOLANO SERRANODIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZ
316-01-0141-4616-005ALTA VERAPAZCOBANESCUELA NACIONAL DE CIENCIAS COMERCIALES2A CALLE 1110 ZONA 279514215RUDY ADOLFO TOT OCHHÉCTOR ROLANDO CHUN POOUDIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZ
416-01-0142-4616-005ALTA VERAPAZCOBANINSTITUTO NORMAL MIXTO DEL NORTE EMILIO ROSALES PONCE3A AVE 623 ZONA 1179521468RUDY ADOLFO TOT OCHVICTOR HUGO DOMÍNGUEZ REYESDIVERSIFICADOOFICIALURBANAABIERTABILINGUEVESPERTINADIARIO(REGULAR)ALTA VERAPAZ
516-01-0143-4616-031ALTA VERAPAZCOBANCOLEGIO PARTICULAR MIXTO IMPERIAL5A CALLE 19 ZONA 357101061MERCEDES JOSEFINA TORRES GALVEZHÉCTOR WALDEMAR TOT COYDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAALTA VERAPAZ
616-01-0145-4616-006ALTA VERAPAZCOBANINSTITUTO DE TURSMO Y AVIACON DEL NORTE I.T.A.N3 AV 528 ZONA 454641454EFRAIN CAAL CUCLUIS FERNANDO SOTODIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEMONOLINGUEMATUTINADIARIO(REGULAR)ALTA VERAPAZ
716-01-0147-4616-031ALTA VERAPAZCOBANCOLEGIO LA INMACULADA7A CALLE 1109 ZONA 6 COBAN49532425MERCEDES JOSEFINA TORRES GALVEZROSA MARÍA ZAMORA GONZÁLEZDIVERSIFICADOPRIVADORURALCERRADA TEMPORALMENTEMONOLINGUEDOBLEDIARIO(REGULAR)ALTA VERAPAZ
816-01-0150-4616-006ALTA VERAPAZCOBANINSTITUTO INTERCULTRUAL ALTAVERAPACENCESE -IIAV-3A AVAENIDA 123 ZONA 4NaNEFRAIN CAAL CUCGUILLERMO ESTUARDO VASQUEZ MORALESDIVERSIFICADOPRIVADOURBANACERRADA TEMPORALMENTEBILINGUEDOBLEFIN DE SEMANAALTA VERAPAZ
916-01-0155-4616-031ALTA VERAPAZCOBANLICEO MODERNO LATINO11 AVENIDA 517 ZONA 479522555MERCEDES JOSEFINA TORRES GALVEZHÉCTOR ARMANDO TEYUL CHENDIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAALTA VERAPAZ
CODIGODISTRITODEPARTAMENTOMUNICIPIOESTABLECIMIENTODIRECCIONTELEFONOSUPERVISORDIRECTORNIVELSECTORAREASTATUSMODALIDADJORNADAPLANDEPARTAMENTAL
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932319-09-0008-4619-021ZACAPALA UNIONINSTITUTO NACIONAL DE EDUCACION DIVERSIFICADA, INEDBARRIO NUEVO46390005.0ASBEL IVAN SUCHITE ARROYOGUSTAVO LEIVA MORALESDIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)ZACAPA
932419-09-0034-4619-021ZACAPALA UNIONLICEO PARTICULAR MIXTO JIREHBARRIO NUEVO79418369.0ASBEL IVAN SUCHITE ARROYOANA MARÍA CUELLAR GUERRADIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEDOBLEFIN DE SEMANAZACAPA
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932619-09-0040-4619-021ZACAPALA UNIONLICEO PARTICULAR MIXTO JIREHBARRIO NUEVO79418369.0ASBEL IVAN SUCHITE ARROYOANA MARÍA CUELLAR GUERRADIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ZACAPA
932719-09-0048-4619-021ZACAPALA UNIONLICEO PARTICULAR MIXTO JIREHBARRIO NUEVO79418369.0ASBEL IVAN SUCHITE ARROYOANA MARÍA CUELLAR GUERRADIVERSIFICADOPRIVADOURBANAABIERTAMONOLINGUESIN JORNADASEMIPRESENCIAL (UN DÍA A LA SEMANA)ZACAPA
932819-10-0013-4619-015ZACAPAHUITEINSTITUTO DIVERSIFICADOBARRIO BUENOS AIRES47097386.0SILDY MARIELA PEREZ FRANCOMARLON JOSUÉ ARCHILA LORENZODIVERSIFICADOOFICIALURBANAABIERTAMONOLINGUENOCTURNADIARIO(REGULAR)ZACAPA
932919-10-1009-4619-015ZACAPAHUITEINSTITUTO MIXTO DE EDUCACION DIVERSIFICADA POR COOPERATIVA DE ENSENANZABARRIO EL CAMPO55958103.0SILDY MARIELA PEREZ FRANCOROBIDIO PORTILLO SALGUERODIVERSIFICADOCOOPERATIVAURBANAABIERTAMONOLINGUEVESPERTINADIARIO(REGULAR)ZACAPA
933019-11-0018-4619-020ZACAPASAN JORGEINSTITUTO MIXTO DE EDUCACION DIVERSIFICADA POR COOPERATIVABARRIO EL CENTRO41447589.0ALBA LUZ MENDEZVICTOR HUGO GUERRA MONROYDIVERSIFICADOCOOPERATIVAURBANAABIERTAMONOLINGUEMATUTINADIARIO(REGULAR)ZACAPA